Keywords
Stock Market Forecasting
NIFTY 50
Technical Indicators
Financial Analytics
How to Cite
Abstract
Objective: To evaluate the effectiveness of a feedforward Artificial Neural Network (ANN) in forecasting short-term returns of the NIFTY-50 index using widely adopted technical indicators.
Method: A quantitative, supervised learning approach was employed. Daily NIFTY-50 data from 2020 to 2024 were used to compute technical indicators such as the Relative Strength Index (RSI), Exponential Moving Average (EMA), and Moving Average Convergence Divergence (MACD). These indicators formed the input feature set of a feedforward ANN with one hidden layer, trained using backpropagation. Model performance was assessed using Root Mean Squared Error (RMSE) on training and testing datasets.
Results: The empirical results show strong predictive performance, with low RMSE values for both training (0.0138) and testing (0.0109), indicating effective learning and robust generalization. The ANN successfully captures nonlinear patterns in market movements, even during periods of heightened volatility.
Contributions: This study advances the financial forecasting literature by demonstrating that relatively simple feedforward ANN architectures, when combined with technical indicators, can effectively model nonlinear dynamics in stock index data. The findings support the applicability of ANN-based models for short-term forecasting and decision support in emerging financial markets.
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